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March 25, 2014
Neville Li
neville@spotify.com
@sinisa_lyh
Storm at Spotify
•@Spotify since 2011
•Recommendation Team
•Data & Backend
•Storm, Scalding, Spark,
Scala…
About Me
March 25, 2014
Spotify in numbers
Started in 2006, available in 55 markets
20+ million songs, 20,000 added per day
24+ million active users, 6+ million subscribers
1.5 billion playlists
!
Big Data
@spotify
600 node cluster
Every day
•400GB service logs
•4.5TB user data
•5,000 Hadoop jobs
•61TB generated
March 25, 2014
What is Storm?
In data-layman’s terms
• Real time stream processing
• Like Hadoop without HDFS
• Like Map/Reduce with many reducer steps
• Fault tolerant & guaranteed message processing
Photo © Blaine Courts http://www.flickr.com/photos/blainecourts/8417266909/
Storm @spotify
•storm-0.8.0
•22 node cluster
•15+ topologies
•200,000+ tuples per second
•recommendation, ads,
monitoring, analytics, etc.
“Never Gonna Give You Up”
Rick Astley Map
!
First Storm
Application
@Spotify
7
RT Market Launch Stats
Other Uses
•Trending tracks
•Email campaign
•App performance tracking
•UX tracking
Anatomy of
A Storm Topology
From play to
recommendation
Social Listening
Take 1
•PUB/SUB
•Almost real-time
•Spammy
•Hard to scale
All characters appearing in this work are fictitious. Any resemblance to real persons, living
or dead, is purely coincidental.
this
 guy
 again!
Social Listening
Take 2
•Hadoop daily batch
•High latency
•M/R aggregation
•Easier to scale
Social Listening
Revamped
•Kafka → Storm → Backend
•Soft real-time
•Aggregate  trigger bolt
•Easy to scale
Getting Data
accesspoint
playlist search storage
social
kafka
What are we transferring?
•TSV logs with version  type (moving to Avro)
•Centralized Schema Repository
•Parsers in Python  Java
•Log parsing  splitting by topic in Kafka
EndSong 21 username:Str timestamp:Int trackId:Str
msPlayed:Int reasonStart:Str reasonEnd:Str …
ClientEvent 15 username:Str platform:Str timestamp:Int
jsonData: Str …
March 25, 2014
Getting Data Across the Globe
Photo © Riley Kaminer http://www.flickr.com/photos/rwkphotography/3282192071/
Ashburn London
Stockholm
San
 Jose
Hadoop Storm
big
 kafka
consumer
LONDON
March 25, 2014
Topology
EndSong
 
filter
kafka
 
spout
metadata
 
decorator
listening
 
trigger
privacy
 
filterZMTP
 
publisher
metadata
prefsfeed SUB
GET
GET
EndSong
Filter Bolt
•Discard some tuples
–Skipped
–Too short
•Keep some fields
–Context
–Reasons
Metadata Decoration Bolt
•tuple.getStringByField(“trackId”)!
•Append fields in output tuple
•[input fields…, “artistId”, “albumId”]!
•Input fields as constructor argument
•Reuse!
monadic!
Async  Batch RPC
metadata
tuple batch
callback
queueupstream
REQ REP
emit
ack
bolt

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